{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>排名</th>\n",
       "      <th>城市</th>\n",
       "      <th>月均工资</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>北京</td>\n",
       "      <td>9240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>上海</td>\n",
       "      <td>8962</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>深圳</td>\n",
       "      <td>8315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>广州</td>\n",
       "      <td>7409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>杭州</td>\n",
       "      <td>7330</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>宁波</td>\n",
       "      <td>7000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>佛山</td>\n",
       "      <td>6889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>东莞</td>\n",
       "      <td>6809</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   排名  城市  月均工资\n",
       "0   1  北京  9240\n",
       "1   2  上海  8962\n",
       "2   3  深圳  8315\n",
       "3   4  广州  7409\n",
       "4   5  杭州  7330\n",
       "5   6  宁波  7000\n",
       "6   7  佛山  6889\n",
       "7   8  东莞  6809"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data=pd.read_table('./pysourse/salay.txt',sep = '\\t')\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>排名</th>\n",
       "      <th>月均工资</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>城市</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>北京</th>\n",
       "      <td>1</td>\n",
       "      <td>9240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>上海</th>\n",
       "      <td>2</td>\n",
       "      <td>8962</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>深圳</th>\n",
       "      <td>3</td>\n",
       "      <td>8315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>广州</th>\n",
       "      <td>4</td>\n",
       "      <td>7409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>杭州</th>\n",
       "      <td>5</td>\n",
       "      <td>7330</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>宁波</th>\n",
       "      <td>6</td>\n",
       "      <td>7000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>佛山</th>\n",
       "      <td>7</td>\n",
       "      <td>6889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>东莞</th>\n",
       "      <td>8</td>\n",
       "      <td>6809</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    排名  月均工资\n",
       "城市          \n",
       "北京   1  9240\n",
       "上海   2  8962\n",
       "深圳   3  8315\n",
       "广州   4  7409\n",
       "杭州   5  7330\n",
       "宁波   6  7000\n",
       "佛山   7  6889\n",
       "东莞   8  6809"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = data.set_index('城市')\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x0B3E4790>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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avjz75lLmLV3N/GXJ7a+vLGZVxpk6rYta0G+74i2/CEraM7B7e7p3aEMyoZhVJZ1C8C/p/TmSNgA9gaqGIT0eGB8RG4DHJHVPp+Q8Hvh5OiPbPSTJ3izvONnnsW7tW/PFEb23iEUES1d9woJla5i/bBXzlq1mfvpl8PTspazfWL5p2Q5tWjKgpJiBJR02fRkctHN3urZv3di7kvcknQW8BrQHhqWTkL8OXBoRc4G+wP0ZL1kM7JDGFwJExBpJayR1jYjlGeseA4wB6NevX2Psjtl/cLJvYiSxfce2bN+xLZ8d2G2L5zaWB+98tHbTUcD8ZauZt2w1ry5azqTX3qE8oEtxK7511BBOHNmXFu7+AUDSVcDJwOcjYgmwnaQWwGXA3cD+QGugPONl5cDGrcQ3iYg7gDsASktLfXm05YSTfTNS1EL07VZM327FHLhz9y2e+2TDRma98zFjH3qdK/86nb9MLeOGL+3OLj075qi0+UHSrSSt+f0jYk1FPCLKJd0OfCcNLQEyD7N6AWUZ8bcktQNaRoQHOLK847NxCkSblkXs2a8rfxqzLz86YQ/mvr+Ko3/xLDc9/AZr1hfm1bqS9gF2iYgzKxK9pB6S2qeLnA68lN6fBJwhqUjS4cCciPgwjZ+Vsfx9jbcHZtlzy77AtGghTirty2FDe3DTw69z29Nv8fdp7/C9Lw7j0KE9cl28xjYCKJWUebnyOODr6Y+1c4Hz0vhE4CBgHvABcFoavxW4S9Ki9LmTG6PgZrXlZF+gurVvzY9OGM4JI/vy7YnTOefuKRwxrAfXHjOMXl3a5bp4jSIibgNuq+KpH1SxbDlwcXrLjK8DTm2QAprVI3fjFLjPDuzGpIsP4Mojh/D0nKUc9tOn+fUz8/h0Y3nNLzazJsPJ3mjdsgXnj96Rxy47iH0GbceND73OMbc8xytvV3W6uZk1RU72tknfbsWMO6OU204fyYq1n/Jf//dPrp443XPjmjUDTva2BUkcuVtPHrv8IM7ZfyB/enkRh9w8mYn/LvMMWmZNmJO9ValDm5Zc84VdeeCi/enbrZjL/jSN0379L+a+vyrXRTOzbVBjspd0esYIgHMlrZZ0oqRLJL0tabakozKW98iAzciwXp352/n7ceNxuzHznRUc9b/PcPOjs1n3qUfiNGtKakz2ETEhIgZHxGBgJMm5xK8BFwLDgOOAcZJaVRoZ8HKSc5Zhy5EBx+HBopqUFi3El/fuzxNXjOaYPXpxy5Nz+dzPnmHy7PfdtWPWRNT2PPvLgduBY4B7I2IlMEvSApIvAo8M2Ix179iGn548ghNG9uGa+2dw5l0v06W4Fbv37szwPl3Yo09nRvTtwvad2ua6qGZWSdbJXlJbksvB9wDGAjMyni5j8wiA2zQyYLoNjw7YBOw3uISHLzmA+199h1cWLmda2Qr+7+m32FietPJ7dmrLHn06M7xv8gWwR+8udC5uleNSmxW22rTsTwYejojVkmo7AmCNIwOCRwdsStq0LOKk0r6cVNoXgLXrNzJryQqmLVrBtLKPeK1sBY/Oem/T8gO2K2aPPl0Y3rcLw/t0ZlivzrRrXZSr4psVnNok+1PZfBl55REA+wCLqoh7ZMAC0a51ESP7d2Nk/83DLq9Y8ynTF1ck/494ecGHPDDtHSAZoXOn7Tsk3T99k26gXXp2pFWRTxAzawhZJft0FMCRwHNpaBLwO0k/AfqTTO/2ahq/IO2XP4R0ZEBJFSMDPoNHBiwYnYtbMWqnEkbtVLIp9v7H65hWtoLXyj5iWtkK/jHrXf40ZREArYpEv27FDEqnXBxY0p5B3ZOJV0o6tPasW2Z1kG3LfgQwMyI2AkTEVEkTgJnAOuDc9MdXjwxoW7V9p7YcvmtbDt81GWEzIlj04VqmlX3ErCUfM2/pqmTWrTlLWb9hc89fx7YtM6Zd7MCg7punYGzfxuP5mdUkq/+SiHgeGF0pNpbkh9rMmEcGtFqRRL/tium3XTHHDO+1KV4x61Yy7eKqTbNuvbxgOfdPe4fMMz57dmq7ad7dQSXt0y+CDvTp2s7dQmYpN4ksL2XOunVQpVm31n26kQUfJHPvzlu2Op2QfRUPT1/C8oxxfPpvV8zT3zi4sYtulpec7K3JaduqiCE9OzGkZ6f/eG756vXJ0cCy1ZSX+4QuswpO9tasdG3fmpHtWzOyf9dcF8Usr7hD06yOJJ0kaX46dtTZuS6PWVXcsjerA0kdgZuBfUguFHxV0t8jYmluS2a2JeXrQFaSlpIOsVCFEmBZPW6uPteXz2XL9/U1Ztn6R0T3ap7LmqQTgC9FxOnp498DD0TEHzOW2TQMCLALMHsbNlVf701zXE8+lSUf1lNl3c7blv3W/hElTYmI0vraVn2uL5/Llu/ry+eybcWmcZ9SFeNEbZI5DMi2qq99aY7ryaey5ON6KrjP3qxushr3ySzXnOzN6qa6caLM8kpTTfZ1OiRu4PXlc9nyfX35XLbq/AM4QtL26dwN+wGPNsB26mtfmuN68qks+bgeII9/oDVrKiSdCXwnffg/ETExh8Uxq5KTvZlZAWiq3ThmZlYLTT7ZS7pcUudG2E5PSUMbcP2tJQ2p53W2k1Qvn7GkkZKK67iOep2aSom963OdTUVD1fuGquf1Vb/ro07XtS7XVz1u7Pqbt8le0vPp38GSMi9Q2U/SFypuwO7ADzNjkvavZp1lVcRKJd0oqZWkXSW9IGlEpdsAklPsfi3pK5JmSHqx0u1NSZdKukHSPum6H0z/Tk7/fqu6sgFdgPsk/cfoXpIezbj/XMb9CWnZKh5fKunIjJceAfzvtq6vkkOBiyqtZ29JN6ezj2XGn6OS9MfLP6T3z5R0iqRvSjqoqo1JuirtC9+ao9l8sVLl1/9K0nOSXpb0fnr/OUnvSPp3ev/OGtbf6Oqr3m9rXQcG0TD1fFvr9/caoE5vc12u53rcqPU3by+qAtpUDkgqBQ4mmci8wtPp35KMWNY/RETEFEkDgWNJZtH6DVD5Qob5EfGEpCOAw0lm5PoxyUQtz6Z/y0gqdGY5ulRaT3tg08zbki4m+bA3pKG2wDPaPCPTUxFxGVBjK0TSvsCuQCdJfdJ9IX08Ob1/U0Q8kuX6TgCuBtanoRZASDo+fdyaZD6D6cBxks4BKlo8u6fbbAXMjYgzSOYzeDaN9yQ5H70tcFr6DzQCOD8irq2mPN+PiO+k928nmTltd2CGpClVvOShiBglqTdwCcksa0tIksVfIuKNmt6DHGmwep9tXSd5j+pcz+uhfpeQ1LsNdazTR0k6j3qoyyQz8tWpHgPbk4P6m1fJXtIPSSoUbHkV4g5pK6cVcAXJRCpnVrOacRExqRbbPJvkVLlzSN7MMcAaNv/jdIiIUkn/C7wcERMkvQc8CFwGfJqWp2Vavs8BX5J0XRab7wb8JCLGV1O2EklHp/e/ADyc3j8YqFxBugH9gNXAxIj4j2/9Wq6vC/CbiPhlNWW7CBgC/C4iFioZEuCwiNgg6bmIGC2pH3CxksPuc4AREXFL2tJZBwwAXmTzZ/lfQJX/JMCJpGe8RMTXJO0K/CIiDktbbr+MiC9klG/vSq2yY0mS0ELgyDThfCsinq1me42msep9NnUdeB7YoZ7qeV3rd2uSaxjeo251uhP1VJeph3ocEUNyUX/zKtlHxJUV9yt92/UGrk5nzCJ9c26IiMczX58e7m3t8uLWkl7NeLyBpMX5S+AMkgnVzyb54AdGxI8kPZEuezUwXtJikvl1TyD55/hxRJybthxeAt4BHoyIFyVdUpv9r8KRQMWwEXuz+fMaTNKK3yQiJkm6gaTFN1PSN0gOEwE+jIjja7M+4G9svZvvj+lrJkj6Yhp7QlKwuTUESSU9m2RMmF6SngXWklxl2g44haSFdNVWtlWVbwI3beX5rsAjJOcqt6703HqSKTO3q+U2G0QD1fttreuNWc9rqo/vAHsC71O3Ov0IWx4VVZZtXf4L9VePG73+5lWy34opFRW+jtZHxIgq4l9KW1cnk1SujUBLSccCu0n6c0ScKOkkkkPL75McVrcDemd8A28az0dSazYfNlanJZsPcatyJMnh5YnAXcD1afwPwESSFk/F9j6Tbn83kjl/WwFnpC2Vx2u7PpJkMDLjkLuY5PBzQcYy04ED0/mHAa4BjkhbQh1ILjhaSTIi5KMkk9PfQ/JP/DbJP9iLJPMS13bApy+RfDaQ/DP0q5QorwAeSPd1h0qvXZKWZ1Utt9nY6lLvt6muA+OB+qrnda3fG0iODupap78J7FIPdXkE9VePG7/+RkRe3kgqOiTf0H/MiHclqYyPApNJPqT56f2HgWO2ss6yauK/Jvnwn0sfXwn0Su8/nrHcBGDn9H4n4D6SD/tzGcvclN4uIulbA5ic/r0BGJ2x7G3A57dS3r2AzsCj6eMWGWVsmZZnQPr4PpJKfyTJ4Fx/AEoy96E266uiLKXA+Cri95N0+TwNiKRvsQtJojg7Y933kRzezgXeAqaR/HO8mn52Y4AZGeu9Cjgz4/EbVWx7v/TvAJLuhiKgdxq7OC3L1m5X5rqeN1S9pw51nXqq59S9fi8iSfL1Wqe3tS7Xdz1u7Pqbd2fjSNpB0rVAjyqeKyap2C+QtBiPB74FjCP59p4OPFH5dVnYH/iAzQNaTQS+nN5vkW67HTAKmJv+aHIXcAvJP98XJZ0o6V6Sw7y3SPo4a/oRpRSYUd2TEfEKcCDJ/hLJhO4Vz1VuMd1AcrhLRCwiaREtr7TM8bVYX40ktSRp5bQmOawOkr7Kx4BdIuI3meuOiL9GxGCS9+VgkkPUS0laUf/xedew7YuAK7XlaXA9gYmSDoqIX0TEqIgYRfIZHgT8nCSBVsT/Utt9biiNWO+3WtfruZ7XtX5PT8va4HU6m7pcn/U4F/U3r5K9pF+S9BVnHg5tJOkzhKR/cW+Sw5ubI+JDkkO7iIiXgNeB5yV9thbbHA7MIu0XS/vn7gCOlvQCMDztkzwCeBw4DLiX5Bu7oh/wUpLTud6KiOOA3wJnkRwSV7fdPYCiiHi7hiJetLX1VIiITYeAkgYD70XExs0hieQMgKzWV4VObDm6IyRJ4eV0nX9QclrdsSR9mf1UxeloSs5oWpV+dpAkmvHAM9kWRNJZwBeBk9N9LCapA4uBY4CbJJ2txDeAf6XLlbP5c24D/C7bbTakxqr32dR1ksReH/V8EHWv3xV1pL7rdJ3rcl3qca7qb7712d8QEe8CZPzoUwZ0lDSN5E25luQbbqWkl0i+Dc8HiIi70gq8Ll3HnWz5w9WySj9aQVLBL46ItSQtBJScI/xzkkpxdUTcLulSksr/MnBkRKxUepFIRHwKfD3t24Pk7IU/R8Tc9PFZVezrjcB1W3szJJ0CPBkR87e2XCUtSLpzLsuIvU9yZsLfgA21WZ+kW0hOw+sG/Help0cA/yJpXb1Echj7s4i4RNJuwI2SvhwRY9J19QO+S1KhK9wTEeelz6+r1G9Z0QKCpNVVoQ3JP2IXSU+RJMWfAkTEe5IOJ/nBbArJ2SXfSF83HfiJklPuWgN3Z/s+NLD6qPdHAL+RVNGa3aa6TtI/P4u61/Nzqb/6XS91uj7qcrqeutbjnNRfj40DSGqVVuTMWEtgY2zDGySpdURs9cdZSV0i4qParjvL7Wtbyr219UHS9Kjm+RYk/Y3lGS2vLcoiqWOaOIoqL9MQJPUlaXlVPuwvaPVZ17dWz+u7ftdXna5rXSY5FbvB63FD1F8nezOzApBv3TiblJSUxIABA3JdjCZj9erVtG/fPtfFaDKmTp26LOphDlqzpiJvk/2AAQOYMqWqq4itKpMnT2b06NG5LkaTIam6yezNmqW8OhvHzMwahpO9mVkByNtuHKuaNl/ynTX/CG9mbtk3MdVdCt3/yge3dgm+mRU4J3szswLgZG9mVgCc7M3MCoCTvZlZAXCyNzMrAFkle0lXS5ojabbSabskXSLp7TR2VMayN0kqkzRd0sg01lLSeEmLlcxQP7BhdsfMzKpS43n2Sibu/SLJ8KglwHOSFgAXAsNIZpB5XFJ/4ACScaEHkAzqP45k6NCvkszA3odk6NOfkUzLZWZmjSCbi6pKSaYAWwsskvQayTRh90bESmBWmvxHkowFPT6d0WVXAN8AAAmXSURBVOUxSd0l9UzjP0+Hur2HJNn/ByWzuo8B6NGjB5MnT67b3hUYv19mVp1skv1M4HpJPwA6kkxSvDPwk4xlykgmxe1LMo9jhcUZ8YUAEbFG0hpJXSuP1RwRd5DMnENpaWl4YK9aeGSSB0Izs2rVmOwj4iFJ+5LMmjILeA0YyJbTepWTTKPWupZxMzNrBFn9QBsR34mIIRFxPEm/+x+B3hmL9CGZCX5JpXgvklb/priSCY1bRsTHdS++mZllo8Zkn55J0z69PwaYDzwInCKpWNJQkjkdXwUmAWdIKkrnUZyTTsg7ic3zU54O3Ff/u2JmZtXJps++GJiatsinAWenk+JOIOnPXwecm/74OhE4CJgHfACclq7jVuAuSYvS506u5/0wM7OtyKbP/mNgpyriY0lmfM+MlQMXp7fM+Drg1DqV1MzMtpmvoDUzKwBO9mZmBcDJ3sysADjZm5kVACd7M7MC4GRvZlYAnOzNzAqAk72ZWQFwsjczKwBO9mZmBcDJ3sysADjZm5kVACd7M7MCkM0Qx5Yjw69/lBVrP816+QFXTcpquc7tWjHt2s9ta7HMrAlyss9jK9Z+yoKbjs5q2cmTJ2c9B222Xwpm1ny4G8fMrAA42ZuZFQAnezOzAuBkb2ZWAJzszcwKgJO9mVkBcLI3MysATvZmZgXAyd7MrAA42ZuZFQAnezOzAuBkb2ZWAJzszcwKQFbJXtI5kmaktzPT2EmS5kuaK+nsjGUvkfS2pNmSjsqI3ySpTNJ0SSPrfU/MzKxaNQ5xLKkL8G1gD5Ivh2mSngBuBvYBNgKvSvo70Am4EBgG9AUel9QfOAAYBQwADgbGASPqe2fMzKxq2YxnvxZYARQDApYD+wJPR8RiAElPAocCfYB7I2IlMEvSAmAkcDwwPiI2AI9J6i6pZ0S8m7khSWOAMQA9evRg8uTJdd/DJi7b92DVqlW1er/83poVlhqTfUR8IulOYAFJsr8c6A0szFisDNiBpDU/o5r4/RnxxWl8i2QfEXcAdwCUlpZGtpNxNFuPTMp6QpLaTF5Sm/WaWfNQY5+9pL2Ac0ha7f2AC0i6a8ozFisn6c5pXcu4mZk1gmy6cQ4DHomIDwEkPQIESeu+Qh/gX0CXKuKLgCWV4r1IWv1mZtYIsjkb5w3gYEltJXUg6ZtfCBwhaXtJPYH9gEeBScApkoolDQW6Aa+m8TMkFUk6HJhT8eVhZmYNL5s++wck7Q7MTkO/i4i7JQl4IY1dERGrgamSJgAzgXXAuRERkiYCBwHzgA+A0+p7R8zMrHrZdOMQETcCN1aKjQfGV7HsWGBspVg5cHF6MzOzRuYraM3MCoCTvZlZAXCyNzMrAE72ZmYFwMnezKwAONmbmRUAJ3szswLgZG9mVgCc7M3MCoCTvZlZAXCyNzMrAE72ZmYFwMnezKwAONmbmRUAJ3szswLgZG9mVgCc7M3MCkBWM1VZbnQcehW7331V9i+4O9v1Ahy9LUUysybKyT6PrXz9JhbclF1Snjx5MqNHj85q2QFXTapDqcysKXI3jplZAXCyNzMrAE72ZmYFwMnezKwAONmbmRUAJ3szswLgZG9mVgCc7M3MCoCTvZlZAagx2Us6XdLcjNtqSSdKukTS25JmSzoqY/mbJJVJmi5pZBprKWm8pMWSXpQ0sCF3yszMtlRjso+ICRExOCIGAyOBecBrwIXAMOA4YJykVpIOAUYBA4DLgXHpar4KtAX6pLGf1fN+mJnZVtR2bJzLgduBY4B7I2IlMEvSApIvguOB8RGxAXhMUndJPdP4zyMiJN1DNcle0hhgDECPHj2YPHnyNuxS85Lte7Bq1apavV9+b80KS9bJXlJb4HRgD2AsMCPj6TJgB6AvcH9GfHFGfCFARKyRtEZS14hYnrmNiLgDuAOgtLQ0sh3Yq9l6ZBJnPrI6y4UFZLds53atsh40zcyah9q07E8GHo6I1ZJaA+UZz5UDG4Haxm0rsh3xEpKRLGuzvJkVltqcjXMq8Of0/hKgd8ZzfYBFVcR7kbT6N8UltQNaRsTH21hmMzOrpaySvaT2JH3yz6WhScApkoolDQW6Aa+m8TMkFUk6HJgTER+m8bPS154O3FeP+2BmZjXIthtnBDAzIjYCRMRUSROAmcA64Nz0x9eJwEEkZ+x8AJyWvv5W4C5Ji9LnTq7HfTAzsxpklewj4nlgdKXYWJIfajNj5cDF6S0zvo6kG8jMzHLA0xI2MZKqf+6HVccjooFKY2ZNhYdLaGIiosrbU089Ve1zZmZO9mZmBcDJ3sysADjZm5kVAOVrn66kpaRDLFhWSoBluS5EE9I/IrrnuhBmjSVvk73VjqQpEVGa63KYWX5yN46ZWQFwsjczKwBO9s3HHbkugJnlL/fZm5kVALfszcwKgJO9mVkBcLJvYiRt8+B1kgZI2jfj8Xe0tZHVzKzZcJ99DkkaDLwIzK30VA/g+ogYL+lS4I2IeERSZ+DBiDig0np2Af5Uw+Y+TzL3wATgN8ArwL+Blystd3NEPLxNO2RmectDHOfe4xFxSmZA0kXVLHsK0CmdOAagLfCriHiSZIKZitdfBSyLiDsrr0DSMcAg4GbgQGBtuo7DJD0EvFnXHTKz/ONkn8ck3QIcDayRNAQ4DvhsRHySPn8RyTAJtXEuydSQp5JMBH838JakbwOXRETlowwzawac7HNLwGGSXqwUr+jG+W9JbwFvAOXAeuAeSZdGRBnQBvgk641JN5JMAn8UcBvQGTg/ImZI+hrwR0m/jIi76rxnZpZXnOxzqx1wf0SckxmsqhsnIh4FHpX0AnCHpKNJunHW1WJ7PwZGAY+QJPoVwG0Zv9GOBSbXch/MrAlwss+tnsDR1bXsMwOSWqRz/C4AHgf+CTxP0ueelYj4SFJP4M70x9+DgPUR8YKkM4GuEfHBNu+NmeUtJ/vcKgW+ExG/zgxWtOzT0yJ7ACcCh0rqBgwjOZvmUOD7wJo6bH9/YEYdXm9mTYSTfY5IagWcBhxRxdMdgI+AO9O/50XELEm7R8T0jHUMApbXctOvAOWSRpD84HtI2sKflT5nZs2Qz7PPEUkXAoMj4rKM2BUkp1d2Aw6PiHlVvO5g4Jfpw7dJztb5P2BkDZt8AngMOAfoT3KK5Q0R8aakzwJnpuu4KCIqn3tvZk2ck32OSGoDUHEaZUa8om9+a68VUBQRG2q5zS4kXyTzo4oPXlIxsCEi1tdmvWaW/5zszcwKgMfGMTMrAE72ZmYFwMnezKwAONmbmRUAJ3szswLgZG9mVgD+HwT7ypM327R0AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#显示中文可能乱码,把字体换成微软，关键字参数\n",
    "plt.rc('font',**{'family':'Microsoft YaHei,SimHei'})\n",
    "\n",
    "plt.subplot(2,2,1)\n",
    "plt.plot(data[['月均工资']]) #子图里绘制折线图，需要这2行一起执行\n",
    "\n",
    "plt.subplot(2,2,2)  #子图的第二个图  柱状图\n",
    "plt.bar(data.index,data['月均工资'])   \n",
    "\n",
    "\n",
    "plt.subplot(2,2,3)  #子图的第三个图   工资箱线图\n",
    "# plt.boxplot(data[['月均工资']])\n",
    "data[['月均工资']].boxplot()\n",
    "\n",
    "\n",
    "# plt.subplot(2,2,4)  #子图的第四个图  工资直方图  直方图占用的块太大，把它挤到下面\n",
    "# plt.figure(figsize=(10, 4), dpi=40)\n",
    "data[['月均工资']].hist()\n",
    "\n",
    "# plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
